Instructions to use websfactory/Webs-Sejong-31B-v7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use websfactory/Webs-Sejong-31B-v7 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="websfactory/Webs-Sejong-31B-v7")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("websfactory/Webs-Sejong-31B-v7")
model = AutoModelForMultimodalLM.from_pretrained("websfactory/Webs-Sejong-31B-v7")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use websfactory/Webs-Sejong-31B-v7 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "websfactory/Webs-Sejong-31B-v7"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "websfactory/Webs-Sejong-31B-v7",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/websfactory/Webs-Sejong-31B-v7

SGLang

How to use websfactory/Webs-Sejong-31B-v7 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "websfactory/Webs-Sejong-31B-v7" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "websfactory/Webs-Sejong-31B-v7",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "websfactory/Webs-Sejong-31B-v7" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "websfactory/Webs-Sejong-31B-v7",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use websfactory/Webs-Sejong-31B-v7 with Docker Model Runner:
```
docker model run hf.co/websfactory/Webs-Sejong-31B-v7
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Webs-Sejong-31B

🏆 Ranked #1 on the K-AI Leaderboard (leaderboard.aihub.or.kr) Leaderboard entry: Webs-Sejong-31B-R1 Public release: websfactory/Webs-Sejong-31B-v7 Overall average 0.624, ranked #1 as of 2026-07-05. Evaluated on the K-AI Leaderboard, a public Korean LLM evaluation platform operated through AI Hub / NIA, using non-public benchmark data that is not disclosed to participants.

Webs-Sejong-31B is a 31B-parameter Korean-centric language model based on google/gemma-4-31B-it. It is strong at Korean-language knowledge, Korean cultural context, professional and academic reasoning, and commonsense QA, while retaining English capability. This repository provides the same checkpoint that was submitted as Webs-Sejong-31B-R1 on the K-AI Leaderboard.

Highlights

#1 on the K-AI Leaderboard. Overall average 0.624, the top score on the public board as of 2026-07-05 (leaderboard entry: Webs-Sejong-31B-R1).
Korean-first. Strong on Korean cultural and academic tasks, with English ability retained.
Drop-in Gemma-4. Standard Gemma-4 architecture and tokenizer: compatible with the Hugging Face transformers Gemma-4 implementation and expected to work with Gemma-4-compatible serving stacks.

Evaluation: K-AI Leaderboard

Evaluated on the K-AI Leaderboard, a public Korean LLM evaluation platform operated through AI Hub / NIA. Scores are produced on non-public benchmark data that is not disclosed to participants.


Leaderboard entry	`Webs-Sejong-31B-R1`
Overall average	0.624
Rank	#1 (as of 2026-07-05)

Because the benchmark data is not disclosed to participants, this reduces the likelihood of direct benchmark overfitting. Users should still evaluate the model on their own target tasks.

Model


Architecture	Gemma-4-31B (dense)
Parameters	~31B
Precision	bfloat16
Languages	Korean (primary), English
Base model	`google/gemma-4-31B-it`

Hardware

At bf16 the weights are roughly 62 GB. Practical setups:

Full precision: one 80 GB GPU (A100 / H100), or two 40–48 GB GPUs.
4-bit quantized: roughly 20–24 GB for the quantized weights; allow extra memory for KV cache, context length, batch size, and image inputs.

Usage

from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "websfactory/Webs-Sejong-31B-v7"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, device_map="auto")

This model follows the standard Gemma-4 interface. For image-and-text input formatting, refer to the base model documentation at google/gemma-4-31B-it.

Training Details

Training and adaptation details are proprietary and are not disclosed in this release.

Intended Use & Limitations

Intended for Korean-language assistance, knowledge QA, and reasoning. Like any language model it can produce incorrect or outdated information, so do not rely on it for medical, legal, financial, or public-policy decisions without human review. Evaluate it on your own target tasks before production deployment.

License

This model is a derivative of Gemma-4 and is distributed under the Gemma Terms of Use. By using this model you agree to those terms and Google's Prohibited Use Policy.

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Safetensors

Model size

31B params

Tensor type

BF16

Model tree for websfactory/Webs-Sejong-31B-v7

Base model

google/gemma-4-31B

Finetuned

google/gemma-4-31B-it

Finetuned

(213)

this model

Quantizations

2 models